
Low-rank regularization [1]}: Here, one would choose \(\mathcal {R}_1(\mathbf {U}) = \Vert \mathbf {U}\Vert ^2\)  and \(\mathcal {R}_2(\mathbf {V}) = \Vert \mathbf {V}\Vert ^2\)  .

Blind compressed sensing: Here, one would choose \(\mathcal {R}_1(\mathbf {U}) = \Vert \mathbf {U}\Vert _{\ell _1}\)  and \(\mathcal {R}_2(\mathbf {V}) = \Vert \mathbf {V}\Vert ^2\)  .

Smoothness regularization on manifolds (SToRM): The SToRM scheme also relies on a factorization as in (REF ), where \(R_1 (\mathbf {U}) = \sum \sigma _i \Vert \mathbf {u}\Vert _i^2\)  and \(\mathbf {V}\)  is obtained as the eigenvectors of the graph Laplacian matrix of the graph of the data. Both calibrated [2]} and uncalibrated [3]} formulations are available.

